Abstract

Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment.
It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. However,
in vivo animal tests are constrained by time, ethical considerations, and financial burden. Therefore, computational methods for
estimating the toxicity of chemicals are considered useful. In silico toxicology is one type of toxicity assessment that uses computational methods to analyze, simulate, visualize, or predict
the toxicity of chemicals. In silico toxicology aims to complement existing toxicity tests to predict toxicity, prioritize chemicals, guide toxicity tests, and
minimize late‐stage failures in drugs design. There are various methods for generating models to predict toxicity endpoints.
We provide a comprehensive overview, explain, and compare the strengths and weaknesses of the existing modeling methods and
algorithms for toxicity prediction with a particular (but not exclusive) emphasis on computational tools that can implement
these methods and refer to expert systems that deploy the prediction models. Finally, we briefly review a number of new research
directions in in silico toxicology and provide recommendations for designing in silico models. WIREs Comput Mol Sci 2016, 6:147–172. doi: 10.1002/wcms.1240

2D scatter plots of molecular descriptors and toxicity levels. (a) no correlation between molecular descriptor 1 and the toxicity endpoint. (b) and (c) linear and nonlinear relationships between the molecular descriptors 2 and 3, respectively, with the toxicity endpoint. (b) and (c) can be modeled with linear and nonlinear algorithms, respectively.

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